When congestion can be useful: modelling driver diversion behaviour in road traffic networks

The ability to accurately predict driver route choices is an important part of traffic assignment, the process of forecasting traffic flows on roads across a region. Many assignment methods only consider the presence of recurrent forms of congestion, such as during rush hour periods, and fail to incorporate non-recurrent congestion effects caused by irregular events such as road traffic accidents. This paper proposes an agent based driver route choice model which includes driver reactions to the presence of non-recurrent congestion, supposing that drivers learn relationships between congestion locations and adjust their expectation of network travel times en-route, potentially choosing to divert. By simulating an example network with mixed populations consisting of agents capable of diverting and not, the result is found that initially increasing the proportion of diverting agents from zero is beneficial to the system as might be expected, reducing the number of vehicles navigating the incident affected area, but beyond a tipping point agents can no longer perceive the presence of congestion prior to diverting and network performance decreases. The model not only demonstrates the conflict between agents adopting travel time reducing behaviour and its impact on system performance, but it also highlights the importance of modelling driver knowledge appropriately to reproduce plausible phenomena in simulation